Is there a “metabolic-mood syndrome”? A review of the relationship between obesity and mood disorders

Neuroscience and Biobehavioral Reviews 52 (2015) 89–104

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Review

Is there a “metabolic-mood syndrome”? A review of the relationship between obesity and mood disorders Rodrigo B. Mansur a,b,∗ , Elisa Brietzke b , Roger S. McIntyre a a b

Mood Disorders Psychopharmacology Unit (MDPU), University Health Network, University of Toronto, Toronto, Canada Interdisciplinary Laboratory of Clinical Neuroscience (LINC), Department of Psychiatry, Federal University of São Paulo, São Paulo, Brazil

a r t i c l e

i n f o

Article history: Received 23 April 2014 Received in revised form 19 December 2014 Accepted 31 December 2014 Available online 8 January 2015 Keywords: Mood disorders Major depressive disorder Bipolar disorder Subtypes Obesity Metabolic syndrome Diabetes mellitus Metabolism RDoC Research domain criteria

a b s t r a c t Obesity and mood disorders are highly prevalent and co-morbid. Epidemiological studies have highlighted the public health relevance of this association, insofar as both conditions and its co-occurrence are associated with a staggering illness-associated burden. Accumulating evidence indicates that obesity and mood disorders are intrinsically linked and share a series of clinical, neurobiological, genetic and environmental factors. The relationship of these conditions has been described as convergent and bidirectional; and some authors have attempted to describe a specific subtype of mood disorders characterized by a higher incidence of obesity and metabolic problems. However, the nature of this association remains poorly understood. There are significant inconsistencies in the studies evaluating metabolic and mood disorders; and, as a result, several questions persist about the validity and the generalizability of the findings. An important limitation in this area of research is the noteworthy phenotypic and pathophysiological heterogeneity of metabolic and mood disorders. Although clinically useful, categorical classifications in both conditions have limited heuristic value and its use hinders a more comprehensive understanding of the association between metabolic and mood disorders. A recent trend in psychiatry is to move toward a domain specific approach, wherein psychopathology constructs are agnostic to DSM-defined diagnostic categories and, instead, there is an effort to categorize domains based on pathogenic substrates, as proposed by the National Institute of Mental Health (NIMH) Research Domain Criteria Project (RDoC). Moreover, the substrates subserving psychopathology seems to be unspecific and extend into other medical illnesses that share in common brain consequences, which includes metabolic disorders. Overall, accumulating evidence indicates that there is a consistent association of multiple abnormalities in neuropsychological constructs, as well as correspondent brain abnormalities, with broad-based metabolic dysfunction, suggesting, therefore, that the existence of a “metabolic-mood syndrome” is possible. Nonetheless, empirical evidence is necessary to support and develop this concept. Future research should focus on dimensional constructs and employ integrative, multidisciplinary and multimodal approaches. © 2015 Elsevier Ltd. All rights reserved.

Contents 1. 2.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Is the association of obesity and mood disorders a distinct illness subtype? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Phenomenology, course and treatment outcome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Genetic factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3. Environmental risk factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4. Developmental aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5. Metabolic systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6. Brain substrates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

∗ Corresponding author at: 399 Bathurst Street, MP 9-325, Toronto, ON, M5T 2S8 Canada. Tel.: +1 416 603 5800; fax: +1 416 603 5368. E-mail address: [email protected] (R.B. Mansur). http://dx.doi.org/10.1016/j.neubiorev.2014.12.017 0149-7634/© 2015 Elsevier Ltd. All rights reserved.

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2.7. Animal models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.8. Methodological issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Obesity and mood disorders are heterogeneous conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Mood disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Obesity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . “Metabolic-mood syndrome”: a research agenda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Domains of psychopathology associated to metabolic dysfunction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Metabolic dysfunction in mood disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction Mood disorders (i.e. major depressive disorder [MDD] and bipolar disorder [BD]) are highly prevalent syndromes. Global prevalence of MDD is estimated to be around 4–5% (Vos et al., 2012; Baxter et al., 2014), whereas BD affects approximately 1.5% of the population (Kessler et al., 2005; Perala et al., 2007). Both conditions often pursue a chronic, unremitting course, which underscores a major impact in morbidity. Mood disorders are leading causes of years lived with disability (YLDs) and disability-adjusted life years (DALY), highlighting MDD and BD as a public health priority (Mathers et al., 2006; Vos et al., 2012; Whiteford et al., 2013). Overweight and obesity have been a major public health concern in the past decades. Epidemiological studies indicate that approximately half of the adult population in Organization for Economic Co-operation and Development (OECD) countries is overweight and about 18% are obese (OECD, 2013). Moreover, this prevalence has increased in all studied countries and in all population groups, regardless of sex, age, race, income or education level (OECD, 2013). Obesity is associated with numerous complications such as type 2 diabetes (T2DM), cardiovascular diseases, musculoskeletal disorders and several forms of cancer, as well as with excess mortality risk (Flegal et al., 2013; Lu et al., 2014; Tobias et al., 2014) and, as a result, has been a cause of growing illness-associated burden and health care costs (Allender and Rayner, 2007; Kelly et al., 2008; Finkelstein et al., 2009, 2010). Obesity and mood disorders are frequently associated. Individuals with MDD have an approximately 50% higher risk of developing obesity, when compared to the general population (Blaine, 2008; Luppino et al., 2010; Toups et al., 2013). The prevalence of obesity is also substantially increased in individuals with BD (Sicras et al., 2008; McIntyre et al., 2010; Kemp et al., 2014). Conversely, overweight and obesity have been reported to increase the risk of onset of significant depressive symptoms and manic episodes (Mather et al., 2009; Luppino et al., 2010; Vogelzangs et al., 2012; Vannucchi et al., 2014). Considering the high impact of obesity and mood disorders in disability and morbidity, the co-occurrence of these conditions is incredibly relevant from a public health perspective and preliminary evidence indicates that a cumulative effect in the associated burden of illness may occur (Everson et al., 2002; Atlantis and Sullivan, 2012; Kemp et al., 2014). Moreover, accumulating evidence has indicated that the association of obesity and mood disorders is not a simple comorbidity. The term comorbidity denotes the co-occurrence of distinct clinical entities. Epidemiological, longitudinal and clinical studies have reported that these illnesses mutually influence each other presentation, trajectory and outcome. Therefore, a bidirectional, convergent relationship would be a more accurate description. Based on these finding some authors have proposed that this cooccurrence constitutes a distinct illness subtype (i.e. “metabolic mood-syndrome”) (McIntyre et al., 2007; Vogelzangs et al., 2011; Levitan et al., 2012). However, many questions about this association remain unanswered. This review aims to discuss the nature

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of the relationship between obesity and mood disorders and its implications for a subtyping strategy. 2. Is the association of obesity and mood disorders a distinct illness subtype? The concept of a bidirectional relationship between obesity and mood disorders is based not only on the reciprocal predictive risk for each illness, but also on reported differences in clinical manifestations and treatment response linked to this co-occurrence. Furthermore, overlaps of a series of genetic and environmental risk factors, as well as of peripheral and central pathological mechanisms, have been described. 2.1. Phenomenology, course and treatment outcome The presence of obesity has been linked to a distinct and more complicated clinical presentation of mood disorders. In obese or overweight individuals with MDD, when compared to normal weight, there is a significant overrepresentation of atypical features (i.e. mood reactivity, increase in appetite, hypersomnia, leaden paralysis, sensitivity to interpersonal rejection) (Lamers et al., 2010; Cizza et al., 2012; Glaus et al., 2012; Levitan et al., 2012; Toups et al., 2013). In fact, most studies have reported that, in contrast to atypical depression, other specifiers for MDD (e.g. melancholic or undifferentiated depression) have a prevalence of obesity similar to the general population or were more associated to lower weight (Lamers et al., 2010; Cizza et al., 2012; Glaus et al., 2012; Levitan et al., 2012; Chou and Yu, 2013; Lamers et al., 2013; Toups et al., 2013). Due to the cross-sectional nature of the data no conclusions about the causal relationship between obesity and atypical features can be drawn. Although it could be postulated that obesity is a simple consequence of the increase in food intake and lack of exercise associated with atypical depression, results from longitudinal studies indicating that obesity per se is a risk factor for the development of depression challenges this idea. Nonetheless, other phenotypical traits are also associated to higher BMI, including suicide ideation and cognitive symptoms, as well as overall severity of depression (Simon et al., 2008; Marijnissen et al., 2011; Byers et al., 2012). Moreover, obesity/overweight was also associated to a more chronic, persistent trajectory (Vogelzangs et al., 2010; Byers et al., 2012). Evidence from clinical studies in individuals with BD indicates that obesity predisposes to a predominantly depressive illness, insofar the duration of depressive episodes tended to be longer and hospitalization for depression were more frequent (Goldstein et al., 2011). A more severe and chronic course, with higher functional disability, as well as an increased risk of suicide were reported (McIntyre et al., 2008; Calkin et al., 2009). Comorbid anxiety disorders are also more common (Calkin et al., 2009). Furthermore, obese/overweight individuals with BD were shown to have poor cognitive performance, when compared to normal weight patients, with a poor performance in tests measuring attention and

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psychomotor processing speed (Yim et al., 2012). The presence of depressive symptoms also appears to exert a detrimental effect in obese individuals, as it has been linked to a higher BMI and to the presence of multiple medical comorbidities (Goodman and Whitaker, 2002; Werrij et al., 2006; Zhao et al., 2009). Obesity has also been suggested to negatively impact treatment outcomes in mood disorders. Several studies indicated that higher BMI may be a predictor of poor response to antidepressant in individuals with MDD (Papakostas et al., 2005; Khan et al., 2007; Kloiber et al., 2007; Oskooilar et al., 2009; Uher et al., 2009). In the treatment of BD, obesity was also associated to poor response to pharmacological treatment, including lithium and valproate (Kemp et al., 2010). Conversely, comorbid depression has been reported to be a predictor of unfavorable outcome in different weightloss interventions. Evidence from behavioral, pharmacological and bariatric surgery clinical trials indicates that depressed individuals lose less weight than non-depressed patients and also have poorer long-term weight loss maintenance (Legenbauer et al., 2009, 2011; Ohsiek and Williams, 2011; Pagoto et al., 2013). In fact, individuals that respond well to obesity treatments tend to concurrently display improvements in depressive symptoms and good results have been obtained in trials that employed combined approaches (Simon et al., 2010; Fabricatore et al., 2011; Elder et al., 2012; Busch et al., 2013). 2.2. Genetic factors Mood disorders and obesity are highly heritable illnesses. The heritability of MDD has been estimated between 30% and 50%, whereas for BD is around 50–70% (Sullivan et al., 2000; Smoller and Finn, 2003; Kendler et al., 2006). Obesity also manifests a strong familial influence with the heritability estimates for ranging from 50% to 90% (Stunkard et al., 1986; Hebebrand et al., 2003; Bell et al., 2005). Mood disorders and obesity are thought to follow a polygenic mode of inheritance, with multiple genes contributing to their development (Xia and Grant, 2013; Flint and Kendler, 2014; Kerner, 2014). Genetic studies have found an overlap between genetic risk factors that confer vulnerability for both conditions (Afari et al., 2010; Jokela et al., 2012; Samaan et al., 2013). Interestingly, some of these risk factors were reported to have an interactive effect (Fuemmeler et al., 2009; Rivera et al., 2012; Winham and Biernacka, 2013). For example, the FTO (fat mass and obesity associated) gene has been shown to contribute to obesity, however, a study by Rivera et al. (2012) found that this association was moderated by the presence of depressive symptoms. Conversely, a variant in the TCF7L2 gene, which encodes a transcription factor involved in the Wnt signaling pathway, was associated with protection to BD, but this effect became weaker as BMI increased (Winham and Biernacka, 2013). 2.3. Environmental risk factors Obesity and mood disorders share several risk factors. Traditionally, chronic psychosocial stress is considered one of the most important triggers of mood episodes and has been associated to weight gain and subsequent development of obesity (Altman et al., 2006; Kyrou et al., 2006; Horesh and Iancu, 2010). Inadequate diet and lack of physical exercise are the mainstay of weight gain, but emerging evidence have also highlighted a possible role in MDD and BD onset (Lopresti et al., 2013; Sanhueza et al., 2013; Vancampfort et al., 2013a; Lai et al., 2014). Evidence from epidemiological studies reports a high prevalence of adverse socio-economic situations, including poverty, social isolation, lack of support and low education, in both obese and mood disorders patients (Everson et al., 2002; Sassi et al., 2009; Devaux and Sassi, 2013). Nonetheless, one of the more compelling convergent causative factors is

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childhood trauma. A history of physical, emotional or sexual abuse is well established as one of the most impactful environmental risk factors for mood disorders (Nanni et al., 2012; Carr et al., 2013; Watson et al., 2013). More recently, accumulating evidence indicates that these traumatic experiences have also a significant impact in metabolic health and increases the risk of obesity, T2DM and metabolic syndrome in adulthood (Gunstad et al., 2006; Midei et al., 2010, 2013; Pervanidou and Chrousos, 2012; Lee et al., 2014a). 2.4. Developmental aspects Converging evidence indicates that mood disorders and obesity also share developmental pathways. Metabolic diseases are influenced by intrauterine and/or early life environment, insofar as exposure to an adverse early environment (e.g. fetal overnutrition or undernutrition) has been consistently associated with increased susceptibilities to obesity, diabetes and cardiovascular disease in adulthood (Moore, 2010; Schulz, 2010; Poston, 2012; Wu et al., 2012). This phenomenon is referred to as developmental origins of health and disease or Barker hypothesis (Bjornsson et al., 2004; Gluckman et al., 2008). Low birth-weight, which is considered a surrogate of adverse intrauterine environment, and early childhood malnutrition have likewise been associated to a higher risk of depression during adolescence and adulthood (Nomura et al., 2007; Galler et al., 2010; Pereira et al., 2012; Wojcik et al., 2013). Moreover, a longitudinal study reported that childhood obesity is an independent risk factor for adult depression (Sanchez-Villegas et al., 2012). Maternal depression during pregnancy has been linked to worse perinatal outcomes, as well as to independently increasing the risk of adolescent depression (Pawlby et al., 2009; Grigoriadis et al., 2013; Pearson et al., 2013; Raisanen et al., 2014), suggesting that it may also be an indicative of a similarly adverse antenatal milieu. In fact, it has been reported that exposure to maternal depressive symptoms in early life affects adiposity, increasing the risk of childhood overweight/obesity, although this may be related to differences in parental style (Adam, 2006; Wang et al., 2013; Ruttle et al., 2014). Among the mechanisms proposed to underlie the long-term effects of adverse early environment are epigenetic processes (e.g. DNA methylations that can cause changes in gene expression). While a comprehensive overview of this topic is outside the scope of this review, it is worth noting that both obesity and depression during pregnancy have been associated with epigenetic modifications (Guenard et al., 2013a,b; Teh et al., 2014). 2.5. Metabolic systems Endocrine and immune-inflammatory regulation have been increasingly more recognized as core features of metabolic dysfunction and have also been shown to be involved in mood disorders. Glucocorticoids and the hypothalamic–pituitary–adrenal (HPA) axis are involved in the physiological response to acute stress. Parts of its effects are to mobilize stored energy and restore energy homeostasis, through actions in the adipose tissue, glucose and insulin regulation, as well as in appetite and feeding. Dysregulation of the HPA axis has been consistently reported in overweight and obese individuals, insofar as studies have shown an association between abdominal adiposity and abnormal activation of the HPA axis, with both higher and lower cortisol responses to stress being reported (Pasquali et al., 2006; Mujica-Parodi et al., 2009; Jones et al., 2012; Kubera et al., 2012; Pasquali, 2012). Conversely, individuals exposed to chronic stress and, as a result, to excessive cortisol were found to have an increase in visceral adipose tissue (Brown et al., 2004). Abnormalities in the HPA axis have also been reliably demonstrated in mood disorders. Individuals with BD display cortisol hypersecretion in euthymia, depression and mania (Cervantes et al., 2001) and show low reactivity to stress, with a

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blunted cortisol response to psychosocial stress (Daban et al., 2005). Individuals with depression, both remitted and symptomatic, also have an increase in cortisol levels (Vreeburg et al., 2009; Stetler and Miller, 2011). Interestingly, studies that compared depressive subtypes have reported that HPA overactivation is related to melancholic features (Wong et al., 2000; Gold and Chrousos, 2002; Stetler and Miller, 2011; Lamers et al., 2013) and not to the atypical symptoms, as it would be expected due to its consistent association with metabolic abnormalities. Accumulating evidence supports the association between mood disorders and immune-inflammatory dysregulation. The presence of higher levels of pro-inflammatory markers (e.g. TNF-a, IL-6, sIL-2R) in the serum of MDD and BD patients was confirmed by multiple meta-analyses (Howren et al., 2009; Dowlati et al., 2010; Munkholm et al., 2013). Inflammatory imbalances were reported in all mood states, including euthymia, as well as in first-episode, drug-naïve and chronic patients. Importantly, as it is well established that adipose tissue is an important source of cytokines, studies that controlled for BMI found much lower, although still significant effect sizes. The role of adiposity as a moderator of immune-inflammatory dysregulation has been highlighted. Obesity is associated to a persistent, low-grade pro-inflammatory state and meta-analytical work has also reported elevated levels of Creactive protein (CRP) in various populations with obesity (Choi et al., 2013). Likewise the HPA axis dysregulation, the differences in inflammatory profile between subtypes of depression has been a focus of study. Individuals with atypical features, when compared to individuals with melancholic features, display higher levels of pro-inflammatory markers, such as CRP, IL-6, TNF-a (Hickman et al., 2013; Lamers et al., 2013). Adipose-derived hormones are another link between mood disorders and metabolic dysfunction. For example, obesity, insulin resistance (IR) and diabetes have been associated with lower levels of adiponectin, an adipose-derived hormone, whereas individuals with MDD and BD were shown to have altered levels of adiponectin (Zeman et al., 2009; Barbosa et al., 2012; Wilhelm et al., 2013). Another member of this family, leptin, is considered to influence obesity, as it exerts a role in the regulation of nutritional intake and energy expenditure. A role in mood disorders has also been proposed for leptin (Lu, 2007; Zupancic and Mahajan, 2011; Barbosa et al., 2012; Milaneschi et al., 2012). Interestingly, a recent study found that the relationship between leptin and depressive symptoms is moderated by central adiposity, as high levels of both leptin and waist circumference incurred in the highest risk of developing depressed mood over a 9-year follow-up (Milaneschi et al., 2014). 2.6. Brain substrates Mood disorders are known to be subserved by alterations in brain structural and functional connectivity. It has been reported that MDD and BD are phenotypically subserved by a disruption of brain networks implicated in emotion regulation, reward processing and cognitive control. Neuroimaging studies have described abnormal integrity in the neural tracts connecting the frontal cortex with the temporal and parietal cortices, as well as with subcortical regions (Lin et al., 2011; Vederine et al., 2011; Liao et al., 2013). Functional connectivity studies have demonstrated decreased connectivity between ventral prefrontal networks and limbic brain structures, including the amygdala, at rest and during performance of tasks that involved emotional processing (Blond et al., 2012; Diener et al., 2012; Strakowski et al., 2012; Vargas et al., 2013). The networks involved are thought to be prominently involved in the modulation of cognition and emotional control (Blond et al., 2012; Diener et al., 2012; Strakowski et al., 2012). More recently, obesity has also been considered to be, at least partially, subserved by abnormal brain networks. Obesity has been

associated with disturbed connectivity in neurocircuits involved in the regulation of motivation and reward (e.g. fronto-occipital, fronto-amygdala networks), both at resting-state and in response to food and non-food rewarding stimuli, indicating that the abnormalities extend beyond appetite regulation (Garcia-Garcia et al., 2012; Kullmann et al., 2012). Data from neuroimaging studies provides evidence for the existence of interactive effects between metabolic disturbances and mood disorders in brain structure and function. In individuals with MDD reductions in subcortical and white matter areas were associated with increased BMI, but not with current depressive symptoms (Cole et al., 2013). In BD, elevated BMI was shown to mediate the decrease of brain whitematter volume and temporal lobe volume (Bond et al., 2011), as well as impaired structural connectivity in temporal, parietal and occipital areas (Kuswanto et al., 2014). Importantly, both studies were done with individuals recently diagnosed with BD, lessening the impact of confounding factors, such as age and cerebrovascular abnormalities. Several neurotransmitters, including, but not limited to, dopamine, opioids and serotonin, are implicated in the brain reward circuitry and the regulation of mood, as well as in the homeostatic regulation of food intake (Cota et al., 2006; Volkow et al., 2011; Russo and Nestler, 2013). Multiple studies reported that dopaminergic neurotransmission in dysregulated in mood disorders. Evidence from pharmacological studies indicates that manipulation of dopamine receptors induce mood alterations (e.g. drugs that increase dopamine levels, such as cocaine, are associated with mood elevation) (D’Aquila et al., 2000). Moreover, medications that target the dopamine receptor (e.g. bupropion, antipsychotics) have been successfully used in the treatment of mood disorders for decades. Imaging studies have also reported abnormalities in the dopaminergic system in both MDD and BD (Chang et al., 2010; Anand et al., 2011; Camardese et al., 2014). In obesity, preclinical and clinical studies have provided evidence of abnormal dopamine signaling, characterized by low dopamine receptor availability, which was proportional to the subject’s BMI, enhanced sensitivity to conditioned stimuli (e.g. high-calorie food), but a decreased sensitivity to rewarding effects (Volkow et al., 2008, 2011; Anand et al., 2011; van de Giessen et al., 2014). An important aspect of brain function that connects mood disorders and metabolic dysfunction is energy metabolism. Abnormalities in cellular bioenergetics, mainly mitochondrial function, have been implicated in the pathophysiology of mood disorders (Clay et al., 2011; Gardner and Boles, 2011; Marazziti et al., 2011; Manji et al., 2012). Neuroimaging studies (i.e., magnetic resonance spectroscopy) provide further support for mitochondrial dysfunction in mood disorders insofar as altered concentrations of brain metabolites including, but not limited to, N-acetyl-aspartate (NAA), lactate and creatine (Cr) have been documented (Iosifescu and Renshaw, 2003; Stork and Renshaw, 2005; Brady et al., 2012; Kraguljac et al., 2012; Chu et al., 2013; Sozeri-Varma et al., 2013; Xu et al., 2013). Conversely, mitochondrial dysfunction has also been implicated in the pathophysiology of obesity (Vernochet and Kahn, 2012; Thrush et al., 2013). Alterations in the brain’s chemical composition, similarly involving NAA and Cr, have also been described (Gazdzinski et al., 2008, 2010; Schmoller et al., 2010). Moreover, the neurotrophin brain-derived neurotrophic factor (BDNF) is considered to be a key mediator of neuroplasticity (Cowansage et al., 2010; Zagrebelsky and Korte, 2014) and is also involved in energy metabolism (Schwartz and Mobbs, 2012; Marosi and Mattson, 2014). This protein has been reported to influence dendritic arborization, axonal growth and synaptic structure and function (Cowansage et al., 2010; Zagrebelsky and Korte, 2014). Allelic variation of the BDNF gene was associated to brain morphology (Ho et al., 2006; Brooks et al., 2014; Forde et al., 2014) and to cognitive function in the general population (Hong et al., 2011;

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Dincheva et al., 2012; Brooks et al., 2014). Decreased peripheral levels of BDNF levels been consistently reported in both MDD and BD in euthymia, depressive and manic episodes (Bocchio-Chiavetto et al., 2010; Fernandes et al., 2011). BDNF is also involved in cellular bioenergetics, modulating neuronal glucose transport and mitochondrial function (Burkhalter et al., 2003; Markham et al., 2012); and in energy homeostasis regulation, as diminished BDNF expression produced hyperphagia and obesity in a mouse model (Unger et al., 2007) and expression of BDNF in the hypothalamus was shown to be inhibited by dietary restriction and enhanced by energy availability (Xu et al., 2003; Unger et al., 2007). Conversely, the frequency of single nucleotide polymorphism (SNP) Val66Met of the BDNF gene was linked to a higher body mass index (BMI) in healthy individuals (Shugart et al., 2009). Finally, the endocannabinoid system (ECS) seems to be involved in the regulation of energy metabolism and may also play a role in the pathophysiology of MDD and BD (Koethe et al., 2007; Hill et al., 2008; Hill and Gorzalka, 2009; Ashton and Moore, 2011). Multiple animal studies have reported antidepressant-like or anxiolytic-like effects of the endocannabinoids (eCBs) (Gobbi et al., 2005; Mitchell and Morris, 2007; Moreira et al., 2008). Altered levels of circulating eCBs in the serum, as well as decreased expression of type 1 cannabinoid receptor (CB1) in the cortex of individuals with mood disorders were reported (Koethe et al., 2007; Hill et al., 2008; Hill and Gorzalka, 2009). More recently, eCB signaling has been considered a critical regulator of the stress response, through modulatory effects in HPA axis activation and behavioral reactions (Hill and McEwen, 2010; Dlugos et al., 2012; Hill and Tasker, 2012). Endogenous eCBs were also reported to be relevant for stress habituation, interacting with different components of the response to chronic stress (Hill et al., 2010). Evidence from animal and human studies indicates that the ECS modulates mitochondrial respiration in neurons and interacts with systems implicated in the regulation of food intake and body weight, including, but not limited to, interacting with energy-related hormones, such as insulin, leptin and ghrelin (Benard et al., 2012; Bermudez-Silva et al., 2012). Dysregulation in the ECS have been described in obesity, insofar as eCB levels were found to be altered in adipose tissue, as well as in the plasma and saliva of obese patients (Bluher et al., 2006; Di Marzo et al., 2009; Bennetzen et al., 2011; Matias et al., 2012). Moreover, several pharmacological studies reported that chronic antagonism of the CB1 receptor significantly reduced body weight, with concomitant improvements of metabolic parameters (Di Marzo and Despres, 2009; Christopoulou and Kiortsis, 2011). A CB1 antagonist, rimonabant, was approved by the European Medicines Agency for the treatment of obesity, however, its commercialization and development was suspended a few years later due to reports of increased anxiety, depression and suicidal ideation (Di Marzo and Despres, 2009; Christopoulou and Kiortsis, 2011). In fact, animal studies have also described that CB1 antagonists impair the antidepressant or antianxiety effects of eCBs, reinforcing the idea that central ECS activity is relevant for mood states, as well as the interconnectedness of molecular pathways mediating both mood and metabolism (Gobbi et al., 2005; Mitchell and Morris, 2007; Moreira et al., 2008). More recently, research in the ECS as a therapeutic target for obesity has focused on the development of neutral antagonists and peripherally restricted antagonists, as well as molecules that indirectly target eCBs levels (e.g. n-3 polyunsaturated fatty acids), which would still have the potential to ameliorate metabolic parameters, but without the reported negative effects on mood (Le Foll et al., 2009; Silvestri and Di Marzo, 2012; Boon et al., 2014). 2.7. Animal models Preclinical studies have provided further evidence of the interconnectedness of mood disorders and obesity. Chronic stress

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paradigms have been widely used to mimic depression and/or depressive-like behavior in animals (e.g. social avoidance, anhedonia) (Strekalova et al., 2011). In addition to its behavioral effects, chronic stress models have also been shown to impact metabolic processes, being associated to an increase in adiposity, induction of insulin resistance and disruption of lipid regulation (Chuang et al., 2010; Patterson et al., 2013; Lin et al., 2014). Animals fed a high-fat diet (HFD), which readily gain weight, are a model amply used to study human obesity and metabolic syndrome (Buettner et al., 2007). Evidence indicates that HFD mice exhibit a depressivelike phenotype as well, similarly to the observed in stress-based paradigms (Sharma and Fulton, 2013; Hu et al., 2014). Notably, effects of HFD include neurobiological alterations related to depressive phenotypes, such as abnormalities in limbic system activation and reward circuitry, decrease in serotonin and dopamine levels and enhanced HPA axis reactivity (Kim et al., 2013; Sharma and Fulton, 2013; Hu et al., 2014). An animal model of T2DM (i.e. db/db mice, characterized by a mutation in the gene encoding the long isoform of the leptin receptor) was also reported to display behavioral depression (Sharma et al., 2010). Moreover, a study using the Flinders Sensitive Line (FSL) rat, a validated genetic animal model of depression (Overstreet et al., 2005), found that HFD exacerbated depressive-like features in the FSL rats, but did not affect non-depressed rats, suggesting that a more vulnerable rat strain was more susceptible to the detrimental effects of HFD (Abildgaard et al., 2011).

2.8. Methodological issues Notwithstanding the accumulating body of evidence linking obesity and mood disorders, there are important methodological limitations that should be considered. A significant number of studies failed to find an association between these two conditions or could not support the idea that this association incurred in differences in phenomenology, trajectory or treatment response (Papakostas et al., 2005; Dave et al., 2011; Chang and Yen, 2012; Dong et al., 2013; Goldstein et al., 2013; Toups et al., 2013). Inconsistencies in the results have also been frequent in studies evaluating metabolic processes and mood disorders. For example, some studies did not observe alterations in inflammatory markers in MDD (Haack et al., 1999; Steptoe et al., 2003) and most authors agree that not all individuals with depression have a pro-inflammatory profile (Raison et al., 2013). Discrepancies can also be found in the data obtained from animal models, as a study using a chronic social defeat model observed that the depressive features were in fact associated with anorexic behavior (Iio et al., 2012). A separate study documented that rats exposed to mild food restriction had a higher frequency of depressive-like behavior when compared to controls (Iio et al., 2014). This variability in results is meaningful and could be explained by some factors. First, most of the studies did not considered age and gender in the analyses. As it is known that weight and mood disorders, as well as other important clinical characteristics, are distributed differently between age groups or gender (Baskaran et al., 2014), this omission could have biased some of the results. Second, almost all studies used categorical BMI to contrast obesity and normal weight. Although BMI categories are the standard criteria, recent research have highlighted that obesity is characterized by a considerable clinical and pathophysiological heterogeneity. Moreover, it can also be argued that mechanistic studies of a general group of individuals diagnosed with MDD or BD is inherently limited, due to phenotypical and etiopathological heterogeneity of mood disorders that is inextricably linked to its marked interindividual variability in clinical presentation and treatment response. As a result, the identification of subgroups with a more homogenous set of characteristics emerges as a more

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promising strategy to advance our understanding of obesity and mood disorders. 3. Obesity and mood disorders are heterogeneous conditions Obesity and mood disorders are defined by their phenotypic expressions (i.e. accumulation of adipose tissue; altered mood and/or energy levels). Nonetheless, it is well known that the underlying mechanisms leading to the emergence of these symptoms are extremely varied and include several different genetic, behavioral, and environmental factors. Recently, an emerging trend in medical sciences is the subtyping of heterogeneous diseases based on pathophysiological mechanisms (Costa e Silva, 2013; Ozomaro et al., 2013; Yang et al., 2013). This approach has been successfully applied in cancer research, whereas it has been demonstrated that risk factors and treatment response may vary by molecular subtypes (Gonzalez de Castro et al., 2013). For example, a study found that patients with mutated-PIK3CA colorectal cancer benefited from taking aspirin, whereas patients with wild-type PIK3CA cancer did not (Liao et al., 2012). The complex nature of obesity and mood disorders, as well as the challenges in developing effective strategies for prevention and treatment makes them excellent candidates for this approach. 3.1. Mood disorders Mood disorders have a rich history of subtype’s descriptions. Several subgroups have been proposed based on specific combinations of symptoms (i.e. melancholic depression, psychotic depression), onset (seasonal affective disorder, postpartum), etiology (exogenous, endogenous) or severity (Harald and Gordon, 2012). However, the clinical applications of these distinctions were mostly unsuccessful and they were gradually abandoned (van Loo et al., 2012). Currently, two main groups of mood disorders are recognized, depressive disorders and bipolar disorders, with different subcategories within each group (e.g. BD I and II, dysthymia, cyclothymia, substance-induced mood disorders). The diagnostic criteria of mood disorders are based on the combination of different symptoms. A debate about the validity of these diagnostic categories has occurred for decades in psychiatry. Most authors agree that these categories have contributed greatly to the reliability of psychiatric diagnoses and are generally useful in clinical practice to inform treatment planning and prognosis. Nonetheless, they are based solely on phenomenology and do not incorporate developmental and etiopathological features that were recently highlighted by neuroscience research. Genetic and psychometric studies have reported that these symptomatic criteria do not reflect a single underlying factor (Kendler et al., 2013). As a result, two patients can exhibit entirely non-overlapping clusters of symptoms, but receive the same official diagnosis and an identical treatment. Mood disorders are syndromes defined by the presence of an abnormal mood state, either high (mania/hypomania) or low (depression), accompanied by other behavioral, cognitive and neurovegetative alterations. A myriad of pathophysiological processes have been thought to underlie these phenotypical expressions. Currently, it has been hypothesized that the proximal etiological factor is an abnormal structural and functional integrity of neural circuits in distributed networks implicated in cognition and emotional processing (Diener et al., 2012; Strakowski et al., 2012; Phillips and Swartz, 2014). The abnormal brain connectivity is believed to be a consequence of a multifaceted interaction of several genetic and environmental factors (e.g. childhood trauma, psychosocial stress and drug abuse), as well as to involve

different pathophysiological mechanisms that include, but are not limited to, dysfunctions in neurotrophic, immuno-inflammatory and oxidative stress pathways (Strakowski et al., 2012; Moylan et al., 2013; Pfaffenseller et al., 2013). Considering the complex etiopathogenesis and clinical presentation of mood disorders, it is not surprising that treatment outcomes are largely heterogeneous and a significant percentage of the patients do not respond to any of the available treatment options (Perlis et al., 2006; Nierenberg et al., 2010; Bowden et al., 2012). To address this issue, recent clinical studies have attempted to identify more homogeneous and possibly responsive subgroups. For example, the study by Raison et al. (2013) reported that a TNF antagonist (infliximab) did not have a generalized efficacy in treatment-resistant depression. However, when only a subset of patients with high baseline inflammatory biomarkers (i.e. high levels of high-sensitive C-reactive protein) was considered, the experimental treatment significantly improved depressive symptoms, indicating that the subgrouping of individuals and a biomarker informed treatment selection are promising strategies for further research (Raison et al., 2013).

3.2. Obesity Obesity is characterized by excessive accumulation of body fat. Implied in the concept is the notion of a pathological process that will, eventually, culminate in health problems and reduced life expectancy. The World Health Organization (WHO) establishes that a body-mass index (BMI) of 25 kg/m2 or higher is abnormal, wherein a BMI between 25 and 30 kg/m2 is categorized as overweight; a BMI of 30 kg/m2 or more is classified as obese (although there are ethnic differences). Generally, most cases of obesity are thought to be caused by a combination of excessive food consumption and a sedentary lifestyle. However, there is a remarkable variability in how much these factors contribute to cause obesity in different individuals (Despres, 2001; Naukkarinen et al., 2012; Field et al., 2013). Several biological pathways and environmental stimuli are known to be involved in the regulation of body weight, appetite and level of physical activity. Genetic studies have reported specific genes associated to higher adiposity, food intake and response to physical exercise (Ahmad et al., 2013; Fesinmeyer et al., 2013; Tanaka et al., 2013). Hormonal mechanisms for appetite and satiety, as well as energy expenditure regulation are well described (Hussain and Bloom, 2013). The insulin pathway and the development of insulin resistance have been studied for decades (Kahn et al., 2006). Moreover, the adipose tissue, once considered as only a reservoir for lipids, is now recognized as an active endocrine organ that secretes a variety of hormones, including leptin, adiponectin, and resistin, which are involved in energy metabolism (Jackson and Ahima, 2006). The role of the central nervous system (CNS) in obesity has been increasingly recognized (Hussain and Bloom, 2013; Karatsoreos et al., 2013; Jauch-Chara and Oltmanns, 2014). More recently, brain regions and networks implicated in satiety signals, reward to food and addictive behavior; neurohormonal regulatory processes and the effect of mood and psychosocial stress have been described and are currently a focus of research (Hussain and Bloom, 2013; Karatsoreos et al., 2013; Jauch-Chara and Oltmanns, 2014). Taken together, the current evidence supports a disease model of obesity based on the interaction of multiple biological and environmental processes. The existence of a several distinct (albeit interconnected) pathological processes suggests that in different individuals abnormal body weight can be underlied by different mechanisms. This concept is supported by the results of multiple clinical trials for the treatment of obesity, including pharmacological and dietary/behavioral interventions, which showed, at best, small effect sizes and/or important inconsistencies in the results (Gloy et al., 2013; Swift et al., 2014; Yanovski and Yanovski, 2014).

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Another aspect of obesity that has a significant interindividual variability is the presence of metabolic comorbidities, such as T2DM, dyslipidemia, hypertension and cardiovascular diseases. Epidemiological studies have reported a subset of obese individuals that do not present these obesity-related metabolic disturbances (Ferrannini et al., 1997; Karelis et al., 2005; Stefan et al., 2008; Wildman et al., 2008; Primeau et al., 2011). Interestingly, this subgroup, which has been known as “metabolically healthy obese” (MHO), appears to exhibit a different illness trajectory, with a cardiovascular risk and incidence of complications more similar to normal weight individuals (Ferrannini et al., 1997; Iacobellis et al., 2005; Karelis et al., 2005; Primeau et al., 2011; Kramer et al., 2013; Morkedal et al., 2014). Some criteria have been proposed to discriminate healthy from at-risk obese subjects. These criteria include differences in body fat composition/distribution, insulin sensitivity and pro-inflammatory markers; however there is no universally accepted definition of MHO to this date (Primeau et al., 2011; Denis and Obin, 2013). It has also been reported that normal weight individuals may also display obesity associated disorders, which was termed normal-weight obesity (NWO), indicating that BMI is not perfectly correlated to metabolic health (Marques-Vidal et al., 2008; Oliveros et al., 2014). Moreover, the connection between higher BMI with increased mortality rates has recently been challenged. Accumulating evidence has shown that in specific populations, such as individuals with T2DM, chronic kidney disease or heart failure, the presence of overweight may be protective, as this group was reported to have lower mortality when compared to normal weight individuals (Oreopoulos et al., 2008; Jialin et al., 2012; Padwal et al., 2013; Thomas et al., 2014). This phenomenon is known as “obesity paradox” and its significance has been highlighted by findings that in the general population BMIs between 25 and 30 kg/m2 were also predictive of lower mortality (Flegal et al., 2013). Although there are controversies regarding the validity of “obesity paradox” in the general population (Tobias et al., 2014), these results, as well as the existence of the intermediate phenotypes MHO and NWO, suggests that metabolic dysfunction is better conceptualized as a multidimensional construct and it is not just a reflection of weight status. 4. “Metabolic-mood syndrome”: a research agenda It has been proposed that the association between obesity and mood disorders characterizes, in fact, a distinct subtype, the “metabolic-mood syndrome”, whereas alterations in mood and metabolism are clinically connected and mutually influence each other (McIntyre et al., 2007; Vogelzangs et al., 2011; Levitan et al., 2012). However, the methodological issues mentioned above are important limitations and preclude an accurate and definitive description of a subpopulation’s characteristics. Considering the marked heterogeneity of obesity and mood disorders research in the area would greatly benefit from a domain-specific and dimensional approach to the convergent psychopathological and metabolic manifestations. 4.1. Domains of psychopathology associated to metabolic dysfunction Overall, most studies have indicated that individuals with the “metabolic-mood syndrome” have a predominantly depressive illness, with an overrepresentation of atypical features, anxiety and a chronic course. Despite the usefulness of employing clusters of symptoms to define psychiatric disorders, decades of research have shown that this is a limited strategy, as a single symptom can be subserved by disparate pathophysiological process. Within this context, the use of domains of functioning and their constituent

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dimensional constructs, as proposed by the National Institute of Mental Health (NIMH) Research Domain Criteria Project (RDoC) emerges as a promising strategy for research (Morris and Cuthbert, 2012; Insel, 2014). For example, anhedonia, a core feature of mood disorders, has been recently studied as dysfunction of reward, as defined by responses to emotional stimuli such as food, sex and social interaction (Dillon et al., 2014). Some aspects of the brain’s reward circuitry are relatively well characterized, including the neural systems involved in reward anticipation, consummation and learning, as well as its molecular aspects, particularly the regulation of dopamine transmission (Russo and Nestler, 2013). The focus on a core psychological function and the use of an integrated approach, involving behavioral, neural circuit, and molecular levels of anhedonia, allows a better understanding of this phenomena and how it relates to mood disorders course and treatment. The interaction of mood and metabolism would benefit from such approach. In keeping with the example of anhedonia, reward circuits are also known to be prominently involved in eating behavior and have been a focus of obesity research (Petrovich et al., 2005; Baldo and Kelley, 2007; Volkow et al., 2008; Jauch-Chara and Oltmanns, 2014). This raises several interesting questions, including if and how anhedonia moderates the “metabolic-mood syndrome”? Preliminary evidence comes from the study of Keranen et al. (2010), which evaluated the relationship between anhedonia and eating behavior. Anhedonia, regardless of whether the participant had been diagnosed with a mood disorder, was significantly associated with increased food intake, mainly due to higher rates of uncontrolled, emotional and binge eating (Keranen et al., 2010). Other neuropsychological constructs were also reported to be moderated by the co-occurrence of metabolic and mood disorders. Higher rates of anxiety in individuals with both conditions indicate that alterations in the response to stress may also be involved in this association. Impairment in the psychosocial stress response has been recognized as a core feature of mood disorders clinical expression. Stress is a well-established trigger of mood episodes and stressful life events are one of the strongest predictors of illness onset and relapse (Altman et al., 2006; Horesh and Iancu, 2010; Horesh et al., 2011). Chronic stress exposure is associated with a higher number of mood episodes, as well as with greater symptom severity and persistence (Kim et al., 2007). Stress response and habituation also have an important role in appetite and body weight regulation. Acute stress was shown to increase food intake, especially carbohydrates and/or high caloric meals (Oliver et al., 2000; Hitze et al., 2010; Tryon et al., 2013). The consumption of high-energy foods, in its turn, was reported to attenuate the stress response and its negative effects, including an improvement in mood (Bell et al., 2002; Canetti et al., 2002; Pecoraro et al., 2004; Hitze et al., 2010). This effect appears to be modulated by several features, including gender (Oliver et al., 2000) and history of chronic stress (Tryon et al., 2013), insofar as chronically stressed individuals were reported to have an even greater increase in poststress food craving and caloric consumption, as well as a heightened stress-induced negative mood (Tryon et al., 2013). Consequentially, studies have consistently described that exposure to chronic stress is positively correlated to BMI and fat mass (Tamashiro et al., 2011; Pervanidou and Chrousos, 2012; Tryon et al., 2013). Stress response and adaptation is largely related to a myriad of psychological factors and biological pathways, nonetheless, evidence indicates that eating behavior and body composition are important aspects of it. Therefore, it could be hypothesized that individuals with the “metabolic-mood syndrome” would be more likely to have a history of chronic stress (e.g. childhood trauma, which is a shared risk factor for mood and metabolic disorders) and would respond to acute stress with an even more marked increase in food consumption. However, no study has directly tested this hypothesis to this date.

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Evidence indicates that neurocognitive dysfunction is more prominent in overweight/obese individuals with a mood disorder (Yim et al., 2012). Cognitive dysfunction has been consistently demonstrated in MDD and BD and is considered a core feature of mood disorders (Mann-Wrobel et al., 2011; Lee et al., 2012; Bourne et al., 2013; Snyder, 2013). Generalized impairments in several cognitive measures were reported, including, but not limited to, attention, memory, processing speed and executive function (Mann-Wrobel et al., 2011; Lee et al., 2012; Bourne et al., 2013; Snyder, 2013). Several moderators are recognized, such as sociodemographic and clinical features; nonetheless, evidence indicates that neurocognitive impairment is independently associated to mood disorders (Bourne et al., 2013; Snyder, 2013). Conversely, overweight/obesity and metabolic syndrome have been shown to negatively impact a variety of cognitive domains (Gunstad et al., 2007; Taylor and MacQueen, 2007; Gunstad et al., 2010). One of the more intriguing finding from animal studies has been the robust detrimental effect of HFD on cognitive performance (Stranahan et al., 2008; Gault et al., 2010; Porter et al., 2010), as well as the reversibility of the deficits by dietary changes, physical exercise or pharmacological manipulations (Gault et al., 2010; Porter et al., 2010; Pintana et al., 2013; Pipatpiboon et al., 2013; Woo et al., 2013). In humans, impairments in memory, learning executive function and psychomotor speed have also been consistently reported in T2DM (Strachan et al., 1997; Gold et al., 2007; McCrimmon et al., 2012). The relevance of the effects of metabolic diseases in neurocognitive function is underscored by evidence suggesting that weight loss in overweight and obese individuals significantly improves cognitive performance (Siervo et al., 2011). Furthermore, both metabolic and mood disorders have been considered as independent risk factors for the development of late-life mild cognitive impairment and Alzheimer’s dementia (Whitmer et al., 2005; Ownby et al., 2006; Xu et al., 2011; Cheng et al., 2012). Overall, evidence indicates that the role of cognition on the “metabolic-mood syndrome” can be understood in two complementary ways. As metabolic and mood disorders are independently associated to poor cognitive performance, the cooccurrence of these conditions may have a synergistic detrimental effect (Yim et al., 2012), thus, greater cognitive impairment would be consequential. Nonetheless, cognitive dysfunction has also been considered as a vulnerability factor for mood disorders (Correll et al., 2007; Skjelstad et al., 2010). Interestingly, cognitive factors, especially those related to cognitive control, regulation of motivation and reward, as well as the cognitive response to stress, have also been reported to be prominently involved in the development of obesity (Baldo and Kelley, 2007; Mujica-Parodi et al., 2009; Jauch-Chara and Oltmanns, 2014). In that case, neurocognition could be thought to have a more broad modulatory effect in the link between metabolic and mood disorders, concurrently increasing the risk for both conditions and moderating how they affect each other. In conclusion, accumulating evidence indicates that the clinical presentation of the “metabolic-mood syndrome” involves more than just the co-occurrence of obesity and mood symptoms. The association of metabolic and mood disorders seems to broadly impact multiple neuropsychological domains. Preliminary evidence suggests that these effects may be related to several constructs proposed by the RDoC project, including Negative Valence, Positive Valence, Arousal/Regulatory Systems and Cognitive Systems (Morris and Cuthbert, 2012). Further studies, using multimodal and integrative approaches, are still necessary to understand better the multidimensional aspects of the interaction between mood and metabolism.

4.2. Metabolic dysfunction in mood disorders Obesity has been shown to be only one of the several different determinants of metabolic health. Longitudinal studies using the MHO concept have described that metabolic dysfunction, as understood by the risk of obesity-associated conditions (e.g. CVD, stroke and cancer), seems to be also related to the presence of metabolic comorbidities (e.g. insulin resistance, dyslipidemia, hypertension) and to hormonal and inflammatory profiles. Interestingly, a study by Hamer et al. (2012) found that MHO was not associated to a higher risk for depressive symptoms after a 2 year follow-up, whereas metabolically unhealthy individuals (defined by the presence of 2 or more abnormalities in the following: blood pressure, cholesterol, triglycerides, glycated hemoglobin and Creactive protein) did have an elevated risk. Another study reported an intermediate risk of depression in MHO when compared to non-obese and metabolically unhealthy individuals (Jokela et al., 2013). Taken together these studies indicate that the association between obesity and mood symptoms also appears to be dependent on metabolic profile. As discussed above, there are no established criteria for the characterization of MHO. There is also no consensus about how to define NWO and, therefore, there is no single definition of metabolic dysfunction. Most authors agree that metabolic risk is associated to a combination of different clinical factors, including body adiposity (i.e. central obesity, high visceral white adipose tissue), dyslipidemia and hypertension. The co-occurrence of these features defines metabolic syndrome (MS), a diagnosis that was created to assess and predict CVD and T2DM risks. Accumulating evidence indicates that there is also an association between mood disorders and MS, as well as its subcomponents. Individuals with MDD and BD have been shown to have, when compared to the general population, increased waist circumference, higher proportion of visceral adiposity, raised levels of serum lipids and higher incidence of hypertension, as well as, consequently, to meet criteria for MS more frequently (Fagiolini et al., 2005; Veen et al., 2009; Ludescher et al., 2011; Czepielewski et al., 2013; Vancampfort et al., 2013b). However, there is less evidence regarding the interaction of MS with mood disorders when compared to obesity. A meta-analysis of cross-sectional and cohort studies observed a bidirectional association (Pan et al., 2012), nonetheless, the effects of MS on the phenomenology, course and treatment response of mood disorders are mostly unknown. A bidirectional relationship also seems to be case with diabetes mellitus. The rate of mood disorders is increased in T2DM and vice versa, independently of the presence of overweight or obesity (Ali et al., 2006; Barnard et al., 2006). The risk of developing T2DM is increased by approximately 60% in individuals with MDD (Rotella and Mannucci, 2013a) and individuals with diabetes display a significantly elevated risk for developing MDD, independently of other risk factors (e.g., alcohol or drug use, smoking, medical comorbidities and physical limitations) (Rotella and Mannucci, 2013b). Follow-up studies have also reported the presence of altered glucose metabolism prior to the onset of depression (Golden et al., 2008; Hamer et al., 2011). In addition, depressive symptoms are associated with a more severe course of diabetes, including worse glycemic control, a higher incidence of complications and an increased risk of mortality (Lustman et al., 2000; de Groot et al., 2001; Bruce et al., 2013). Conversely, studies have shown increased morbidity in patients with mood disorders and co-morbid T2DM (Ruzickova et al., 2003; Le et al., 2011), whereas, for example, individuals with BD and T2DM were more likely to have a chronic course, rapid cycling and a worse functional impairment, when compared to non-T2DM subjects (Ruzickova et al., 2003). Insulin resistance (IR), which is considered to be a precursor of T2DM, has been understood as a key marker of metabolic

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Fig. 1. Multidimensional aspects of the association between metabolic and neuropsychological phenotypes. Abnormalities in broadly defined domains of psychopathology are bidirectionally associated with metabolic-based medical conditions. This phenotypical convergence is thought to be mediated/moderated by shared pathophysiological mechanisms, including multiple neural systems and peripheral homeostatic systems, as well as to be underlied by common genetic and environmental risk factors.

dysfunction and has been consistently demonstrated to be associated with MDD (Kan et al., 2013). Moreover, two recent clinical trials have reported beneficial effects for adjunctive use of insulin sensitizers (e.g. pioglitazone) in the treatment of MDD, even in the absence of diabetes or metabolic syndrome (Kemp et al., 2012; Sepanjnia et al., 2012). Preliminary evidence indicates that IR prevalence, as assessed by homeostasis model of insulin resistance (HOMA-IR), is also significantly elevated in individuals with BD (Guha et al., 2014). T2DM and IR have been repeatedly shown to impact the brain, as neuroimaging and neurophysiology studies have demonstrated widespread patterns of white matter abnormalities in discrete pathways (van Duinkerken et al., 2012a; Antenor-Dorsey et al., 2013; Reijmer et al., 2013a,b), alterations in functional connectivity (Musen et al., 2012; van Duinkerken et al., 2012b) and in the concentration of brain metabolites (Geissler et al., 2003; Sahin et al., 2008). Similarly to the structural and functional abnormalities in obesity, an interactive effect of diabetes and mood disorders in the alterations in brain metabolites has been reported, as a recent study observed that the levels of NAA and Cr were lowest among individuals with BD and co-morbid T2DM, intermediate in the patients with BD and IR and highest among the euglycemic BD and control subjects (Hajek et al., 2013). More recently, the molecular underpinnings of metabolic dysfunction have been a focus of research. Evidence from longitudinal studies using metabolomic approaches have identified biomarkers that may possibly predate the clinical manifestations, such as obesity, diabetes and CVD, indicating that they may be a more accurate reflection of metabolic status (Wang et al., 2011; Wang-Sattler et al., 2012; Ferrannini et al., 2013). For example, a cohort study found that a combination of the amino-acids isoleucine, leucine, valine, tyrosine, and phenylalanine significantly predicted the

development of diabetes in the follow-up (Wang et al., 2011). Metabolomics methodology has recently been employed in mood disorders and the first studies identified candidate biomarkers that successfully distinguished patients from controls (Sussulini et al., 2009; Zheng et al., 2013). These metabolites include lipids, lipid-metabolism-related molecules (e.g. acetate, choline, and myo-inositol), amino acids (e.g. alanine, valine, leucine, glutamate, glutamine) and energy metabolism-related molecules (e.g. creatine, creatinine) (Sussulini et al., 2009; Zheng et al., 2013). Unsurprisingly, there is an overlap of biomarkers involved in metabolic dysfunction and mood disorders, indicating that integrated metabolomics approaches are a promising tool for further research. Finally, it is important to notice that energy metabolism is tremendously complex, insofar as it is subserved by dynamic and multi-regulated systems, such as insulin-related pathways, adipose-derived and gut hormones, as well as the HPA and the immuno-inflammatory axis. It is recognized that the activation of these systems is not necessarily pathological, as they are an integral part of adaptive physiological mechanisms and allostatic responses. Allostasis refers to broader and more complex modulatory mechanisms, with emphasis on brain control of the primary regulatory processes and on anticipatory physiological and behavioral changes for future events (Schulkin, 2003; Sterling, 2012). Body weight is subjected to allostatic control and weight gain could be perceived as a physiological response, which is certain situations is adaptive (e.g. chronic illnesses) and do not necessarily constitute a dysfunction of the regulatory systems (Tremblay and Chaput, 2012). The existence of the MHO and NWO phenotypes and of the “obesity paradox” indicates that BMI, when used without proper context, may be an inadequate marker, which, in certain specific situations,

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may be even a deceiving one. This observation is of particular interest in chronic diseases of the central nervous system (CNS), such as mood disorders, and should be considered in the interpretation of future studies.

5. Conclusions Emotional and behavioral alterations have been consistently connected to multifaceted metabolic dysfunction. Clinical studies have indicated that not only syndromal mood disorders, as currently defined by diagnostic categories, are associated with energy metabolism abnormalities, but so as more broadly defined neuropsychological constructs, including, but not limited to, response to stress, cognitive function, motivation and reward. Metabolic dysfunction has also been shown to encompass numerous mechanisms that interact with psychopathology, such as glucose and insulin homeostasis, lipids metabolism, sympathoadrenal regulation and the immune-inflammatory axis. Conversely, multiple neural systems that are thought to underlie and possibly bridge both the emotional/behavioral and the metabolic manifestations have been described (Fig. 1). Overall, evidence indicates that the existence of a “metabolic-mood syndrome”, in which the psychopathological and the metabolic manifestations are mechanistically linked and are the predominant clinical presentation, is possible. Empirical and direct evidence are still necessary to support the concepts proposed in this review. To address the issue of heterogeneity in both metabolic and mood disorders, future studies should focus in domains of psychopathology and in multidimensional metabolic dysfunction, as well as in integrated subdomain phenotypes and underlying substrates. The validation of the “metabolic-mood syndrome” concept may have important clinical implications. Several novel interventions have been recently proposed for both obesity and mood disorders and some of those targets metabolism and/or energy-related mechanisms, such as weight-loss interventions, physical exercise, intranasal insulin and insulin sensitizers, anti-inflammatory agents and creatine supplementation (Linde et al., 2011; Chalder et al., 2012; Kemp et al., 2012; Lyoo et al., 2012; McIntyre et al., 2012; Sepanjnia et al., 2012; Busch et al., 2013; Pereira et al., 2013; Raison et al., 2013). Although the clinical trials with these agents have provided promising results, they have been mostly inconsistent and the effect sizes have been modest at best. Therefore, trials with energy-based treatments would greatly benefit from tests in more homogenous populations, insofar as the relationship between mechanism of action/molecular target and pathological processes would be more informed and the likelihood of success would be increased. For example, evidence suggests that BDNF plays a modulator role in the association of metabolic and mood disorders, as a study reported decreased serum BDNF levels in depressed individuals with T2DM when compared to non-depressed diabetics, independently of gender and BMI (Zhou et al., 2013). A separate study testing a weight-loss intervention for obese adults described that reduction in body weight was associated with increase in BDNF (Lee et al., 2014b). In the whole sample, weight reduction was not associated to changes in depression scale scores, but in a subgroup of individuals with greater BDNF increase weight loss was significantly correlated to improvement in depressive symptoms (Lee et al., 2014b), indicating that the positive effect of weight reduction was present only in a specific subgroup of individuals. The emerging field of personalized medicine posits that an individual’s unique physiologic and clinical characteristics are major determinants of vulnerabilities to illnesses and responses to therapies. As discussed in this review, mood and metabolic disorders are extremely heterogeneous and would be better understood through to use of stratified and individualized approaches (Costa e Silva,

2013; Ozomaro et al., 2013). However, in contrast to other areas of medicine in which personalized medicine has been more widely applied, mood and metabolic disorders lack a single defining gene, biomarker, mechanism or even clinical presentation. Within this context, research in this area should consider the complexity and interconnectedness of metabolic systems, both central and peripheral, as well as the impact of compensatory/allostatic responses and individual trajectories. For example, several pharmacological and non-pharmacological weight-loss interventions have reported positive effects of weight reduction in mood symptoms (Simon et al., 2010; Fabricatore et al., 2011; Linde et al., 2011; Busch et al., 2013). 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